Engagement in Focus: How Family, Gender, and Interests Shape Participation

A statistical study on how the new system changes member engagement

Oliver Bisbee, Jayden Luo, Ryan Yu, Junze Du, TUT0111-A

Introduction

Wellspring is a Canadian cancer support organization offering free programs to anyone affected by cancer. In March 2024, they introduced a new registration system to improve accessibility and engagement.

  • This project explores how factors like parenthood, gender, and personal characteristics relate to program participation, using real member and attendance data.

  • Our goal is to help Wellspring understand what drives program engagement and how to better support diverse members.

Scope

  • The population of interest includes all individuals who registered with Wellspring and who participated in, or were scheduled to participate in, at least one program between 2023 and early 2024.

  • This includes members of all ages, genders, and backgrounds across Canada, regardless of cancer type, who accessed Wellspring services either online or in person.

Our analyses focus on observed behaviors and characteristics from this population, as recorded in Wellspring’s program registration and attendance system.

Data Summary

This project uses two datasets:

  1. Member Background: The dataset includes 4,829 members, capturing demographics, cancer history, and engagement metrics. The average age is approximately 56 years, with female members as the majority.

Most members are from Ontario, with the most common cancer type reported being breast cancer.

Engagement varies widely: the median number of sessions attended by members is 2, but some attended more than 250.

Many fields, including LGBTQ+ status, impairments, and program interests, have high rates of missing data.

  1. Service Deliveries: The Service_Deliveries dataset contains 45,254 records of program sessions (attended or missed) for 3,780 members at Wellspring between 2023 and 2024; most sessions were delivered online and most attendance entries are marked as “Present.”

Data Summary

Variables:

  • Member Id: Unique identifier for each member
  • Parent of a child under 18: Whether the member has a child under 18 (Yes / No / Unknown)
  • Gender: Self-identified gender of the member
  • Member start year and member start month
  • Age (years): Age of the member
  • Program Interest: Programs the member is interested in participating in
  • Number of present service deliveries: Total number of sessions attended
  • Number of absent service deliveries: Total number of sessions missed

Research Question 1:

How do age, number of program interests, and membership duration jointly predict total services attended?

This question is addressed by linear regression, as it allows us to model the simultaneous influence of multiple continuous predictors on a continuous outcome.

Method: Multiple Linear Regression

  • Goal: Model the relationship between age, interests, and membership duration with total services attended.
  • Why this method?
    • Quantifies how each factor affects service attendance.
    • Provides coefficients, confidence intervals, and an R² value to measure predictive strength.
  • Implementation:
    • Standard regression techniques in R (lm() function).
    • Model interpretation: “Holding other factors constant, increasing membership duration by X months increases service attendance by Y.”

How Variables Affect Attendance

Results of Data

  • Absolute Age:
    • Older members tend to attend slightly more services
    • The effect is modest compared to other factors
  • Membership Duration:
    • Membership duration: A strong positive association exists; longer membership is associated with higher attendance.
  • Number of Program Interests:
    • Members with more expressed program interests are predicted to attend more services.
  • Outreach Recommendations for Wellspring:
    • Targeted Messaging: Tailor outreach to meet the unique needs of different age groups
    • Retention Strategies: Encourage early engagement and maintain long-term member relationships
    • Program Cross-Promotion: Leverage multiple program interests to invite members to diverse offerings

Research Question 2

  • Question: Do members with children under 18 attend Wellspring services more or less often than those without children?

  • Relevance: Understanding attendance patterns based on parental status helps Wellspring identify potential barriers for parents and adjust services accordingly. Examples include offering childcare at events or making events more child-friendly.

Research Question 2: Table

Attendance Summary by Parental Status
Parent Status Avg. Attended Total
No 70.63 6039
Unknown 38.43 31045
Yes 46.05 9219

Research Question 2: Boxplot

  • Figure 1: Distribution of attendance for members with known parental status (Yes/No). Members with “Unknown” parental status are excluded. Total plotted: 3,780 members.

Research Question 2: Hypothesis Test

  • Null hypothesis (\(H_0\)): There is no difference between the two groups

  • Alternative hypothesis (\(H_A\)): There is a difference between the two groups

  • Using a p-value less than 0.001 from two-proportion testing, there is strong evidence of a difference in attendance between members with children under 18 and those without.

  • We assume these data are representative.

Research Question 2: Summary

  • The box-and-whisker plot shows that attendance for members with children tends to be lower than for members without children.

  • The median number of services attended by members with children under 18 is lower than that of members without children, and their attendance values are more concentrated around the median.

  • Limitations: some members chose ‘Unknown’ for parental status; this may affect the variability shown in Table 1.

  • Other factors, such as work schedules, motivation, and health, may also influence the results but are harder to quantify.

  • Conclusion: Members with children under 18 are less likely to participate in these services compared to members without children.

Research Question 3

How has the new registration system affected engagement by gender?

Why this research question?

We chose this research question because:

  • It is a two-parameter hypothesis testing question.

  • It provides Wellspring with insight into how the registration system is functioning.

The data we get from performing this two parameter hypothesis test will help Wellspring see if the new registration format is affecting client engagement by gender: if there exists a difference between engagement of female clients and male clients. Other gender categories were excluded due to insufficient sample sizes for reliable analysis. Graphs: Table of focused variables from data

Appointment schedule summary by gender
Gender # Total present Total services Proportion of attended
Female 2519 25621 40115 0.6386888
Male 514 3503 4955 0.7069627
NA 1794 5985 10067 0.5945167

Graph part 3: Boxplot

Data/graph Interpretation

From the simulations we ran:

  • Calculated test statistic for the original data is 0.0682739.

  • We can see from the boxplot that the distribution is centered around 0.07–0.08. Thus the p-value is large (not < 0.05), and we cannot reject the null hypothesis of no difference in engagement between male and female members for the new registration format.

  • We must consider possible type II error and the impact of missing or unspecified gender data, as these factors could affect the results.

Final summary

Conclusion: Based on the test statistics, there is no statistically significant difference in engagement between male and female members after the new registration format was introduced.

Limitations of the Data

  • Limited Variables:
    • Only a few predictors (age, membership duration, number of program interests) were included.
    • Additional factors such as socioeconomic status, health conditions, or transportation access might further explain service attendance.
  • Data Quality & Completeness:
    • Potential missing values or measurement errors may impact model reliability.
    • The data might not capture nuances like the frequency or type of service events attended.
  • Temporal Considerations:
    • The analysis is cross-sectional and may not reflect seasonal or recent changes in member behavior.
  • Additional Variables of Interest:
    • Demographic details (e.g., income, education level) to understand broader member profiles.
    • Psychosocial factors (e.g., motivation, social support) that might influence engagement.
    • Detailed event-specific data (e.g., event type, time, and location) to refine the predictive model.

Overall Conclusions

Re-summary

  • RQ1: Members who are younger, have more program interests, and have been registered longer tend to attend more services overall.

  • RQ2: Members with children under 18 attended fewer services on average than members without children, suggesting family responsibilities may limit participation.

  • RQ3: After the new registration system was introduced, engagement among different genders shifted slightly, with a modest improvement among female-identifying members.

Connections & Insights:

  • Personal and social factors such as family obligations and gender interact with system-level changes (like registration updates) to shape engagement.

  • Structural improvements like system redesign can help. But their impact may vary across subgroups.

  • Program interest is a strong predictor of engagement, suggesting that initial onboarding and motivation may be key.

Recommendations / Next Steps:

  • Collect additional variables on employment, caregiving, and scheduling availability to better explain participation gaps.

  • Conduct subgroup analyses across other demographics (e.g., age × gender).

  • Track long-term engagement trends to assess retention beyond the early months.